1. Field of the Invention
This invention relates to methods and apparatuses for predicting probability of disease recurrence following radical prostatectomy using predetermined clinical and pathological factors. The invention includes nomograms that can be used preoperatively and postoperatively to aid in selection of an appropriate course of therapy.
2. Description of Background
Prostate adenocarcinoma is the most common malignancy in males over the age of 50. Clinically localized prostate cancer is most often treated with conservative management (G. W. Chodak et al., N. Engl. J. Med. 330:242-248, 1994; P. C. Albertson et al., JAMA 274:626-63, 1995), external beam irradiation (G. E. Hanks et al., J. Urol. 154:456-9, 1995; M. A. Bagshaw et al., J Urol 152:1781-5, 1994), or radical prostatectomy (M. Ohori et al., J. Urol. 154:1818-24, 1995; G. S. Gerber et al., JAMA 276:615-9, 1996; C. R. Pound et al., Urol. Clin. North Am. 24:395-406, 1997; J. G. Trapasso et al., J Urol 152:1821-5, 1996), and occasionally with therapeutic interventions such as interstitial radioactive seed implantation or cryotherapy. Making a decision among the different management choices for clinically localized prostate cancer would be greatly facilitated if reliable predictors of the probability that the selected treatment would control the cancer long term were available. Currently, there are no satisfactory randomized prospective trials comparing cancer control among alternative treatments. Although clinical trials are underway, even when these trials are completed, all patients with a clinically localized cancer will not have an equal probability of a successful outcome.
a. Preoperative Assessment
Prior to undergoing radical prostatectomy, it is of great interest to the patient to know whether the procedure is likely to be curative. Because the pathologic stage of cancer correlates with the probability of recurrence after surgery, a number of investigators have made efforts based on cohort studies to predict the final pathologic stage of prostate cancer using various parameters. A number of nomograms and algorithms have been formulated in an effort to identify the pathological stage of an individual""s prostatic cancer. For instance, Partin, et al., has developed a nomogram based on pretreatment prostate specific antigen level (PSA), tumor grade, and clinical stage, to aid physicians in making treatment recommendations by predicting the probability of the final pathological stage of clinically localized prostate carcinoma. (A. W. Partin et al., J. Urol. 115:110-4, 1993). This nomogram was based on data for one patient population. However, although this nomogram does discriminate between organ-confined and non-confined cancer, it has difficulty predicting high probabilities of seminal vesicle invasion and lymph node metastasis, which are the pathologic features with the most profound impact on prognosis. (M. Kattan et al., Cancer 79:528-537, 1997). In addition, this type of nomogram, including the updated version (Partin et al., JAMA 277:1445-1451, 1997), does not provide the physician with a simple means of advising a patient of the likelihood of recurrence if a radical prostatectomy is performed.
Another algorithm developed pursuant to a study by Badalament et al., purports to predict non-organ confined prostate cancer. This study found that nuclear grade, preoperative PSA, total percent tumor involvement, number of positive sextant cores, preoperative Gleason score, and involvement of more than five percent of abase and/or apex biopsy were significant for prediction of disease organ confinement status. (R. Badalament et al., J. Urol. 156:1375-1380, 1996).
Another predictor by Narayan et al., uses preoperative serum PSA, biopsy Gleason score, and biopsy-based stage to predict final pathological stage, by constructing probability plots. (P. Narayan et al., Urology 46:205-212,1995). Yet another predictor by Bostwick et al., uses PSA concentrations, optimized microvessel density of needle biopsy samples and Gleason score to predict extra-prostatic extension. (David Bostwick et al., Urology 48:47-57, 1996).
Existing preoperative predictors typically use final pathologic stage as their end point. (A. W. Partin et al., JAMA 277:1445-1451, 1997). This point is problematic in that some patients with apparently organ confined disease will later develop disease recurrence, whereas many patients with non-organ confined disease will remain disease free. (M. W. Kattan et al., Cancer 793:528-537, 1997). Extracapsular tumor extension, positive surgical margins, seminal vesicle involvement and positive pelvic lymph nodes are adverse pathological features. (A. W. Partin et al., Urol. Clin. North Am. 204:713-725, 1993; J. I. Epstein et al., Cancer 71:3582-3593, 1993; A. Stein et al., J. Urol. 147:942, 1992). Yet not all patients with one or more of these findings are destined to have disease recurrence after radical prostatectomy. Of the 462 men evaluated by Partin et al. with either focal or established extracapsular penetration (A. W. Partin et al., Urol. Clin. North Am. 20(4):713-725, 1993), only 80 (17%) had evidence of disease recurrence with a mean follow-up of 53 months (range 12 to 120 months). Similarly, Ohori and colleagues report a five-year PSA progression rate of 25% for patients with extracapsular extension in the radical prostatectomy specimen. (M. Ohori et al., Cancer 74:104-14, 1994). In a study of the association between positive surgical margins and disease progression, Epstein et al. found that only half of their patients with positive margins developed disease recurrence. (J. I. Epstein et al., Cancer 71:3582-3593, 1993). Thus, using final pathologic stage as an end point limits the utility of a nomogram to accurately predict disease recurrence following radical prostatectomy. In addition, although final pathology has been associated with eventual treatment failure, none of the existing predictors allow the physician to accurately predict preoperatively the likelihood of recurrence of cancer in a patient if a radical prostatectomy is performed. This is typically the information of greatest interest to the patient before electing to undergo surgery.
There are several established prognostic factors relating to the risk of recurrence after surgery or radiotherapy or the risk of metastasis or death from cancer after conservative management, including clinical stage (M. Ohori et al., Cancer 74:104-14, 1994), Gleason grade (P. C. Albertson et al., JAMA 274:626-631, 1995; G. E. Hanks et al., J. Urol. 154:456-9, 1995; G. S. Gerber et al., JAMA 276:615-9, 1996) and serum prostate specific antigen (PSA) levels (G. K. Zagars, Cancer 73:1904-12; 1994). Prior to the present invention, these three routinely available prognostic factors had not been successfully combined into a risk profile that could be used to predict, prior to surgery, the probability of recurrence or metastatic progression after surgical management.
b. Post-Operative Assessment
The most common aggressive therapy for the treatment of clinically localized prostate cancer is radical prostatectomy. Unfortunately, approximately one third of men treated with radical prostatectomy later experience progression of their disease. Typically, the first indication that the disease has progressed occurs as a detectable level of serum PSA months or years following surgery. Early identification, prior to detectable PSA, of men likely to ultimately experience progression would be useful in considering adjuvant therapy or, before documented progression, when adjuvant therapy may be most effective. Accurate identification of the probability of recurrence would also be particularly useful in clinical trials to assure comparability of treatment and control groups or to identify appropriate candidates for investigational treatment such as gene therapy.
Traditionally, the judgment of which patients are at high risk for failure following radical prostatectomy has been based largely on final pathologic stage. As noted, final pathologic stage alone (A. W. Partin et al., JAMA 277:1445-1451, 1997) is a problematic variable for judging high-risk disease since some patients with apparently organ-confined cancer will later develop disease recurrence, and many patients with non-organ-confined cancer will remain disease-free (C. R. Pound et al., Urol. Clin. North Am. 24:395-406, 1997). Not all patients with extracapsular extension or seminal vesicle involvement are destined to have disease recurrence after radical prostatectomy (M. Ohori et al., Cancer 74:104-14, 1994; M. Ohori et al., J. Urol. 154:1818-1824, 1995; C. R. Pound et al., Urol. Clin. North Am. 24:395-406,1997; J. G. Trapasso et al., J. Urol. 152:1821-1825, 1994; A. W. Partin et al., Urol. Clin. North Am. 20:713-725, 1993; J. I. Epstein et al., Cancer 71:3582-3593, 1993). Thus, the use of individual pathologic features appears insufficient to estimate probability for recurrence; a method of combining them is needed.
In 1995, Partin and colleagues (A. W. Partin et al., Urology 45:831-838,1995) published a model for predicting relative risk that was derived using 216 men with clinical stage T2b and T2c prostate cancer treated by a single urologist. The model utilized pretreatment serum PSA with a sigmoidal transformation, radical prostatectomy Gleason score (Gleason sum), and pathologic stage as specimen confined or nonspecimen confined to identify patients with a high relative risk of recurrence following surgery. Their model computed log relative risk and categorized patients into low, intermediate, and high. In a validation cohort of 214 patients treated by one of three different urologists at two institutions, Partin was able to illustrate that the model was apparently able to stratify those patients as well, based on their Kaplan-Meier PSA recurrence-free survival rates although no statistical testing of strata differences was performed. Bauer et al. (J. J. Bauer et al., J. Urol. 159:929-933, 1998) recently emulated Partin""s approach with 378 patients but added race as a predictor variable and widened the cohort to include all clinical stages up to T1a through T2c. Another difference with the Bauer model was the cutoffs used to distinguish the risk groups (relative risks of 4.0 and 5.75 for Partin versus 10 and 30 for Bauer). Bauer""s validation cohort of 99 men indicated a difference in survival rates between the low- and high-risk groups but no difference between intermediate risk and either low or high risk. In another recent study, Bauer (J. J. Bauer et al., Cancer 79(5):952-962, 1997) added biomarkers p53, Ki-67, and bcl-2 to the relative risk calculation. Finally, Harrell et al., discloses a nomogram which evaluates estrogen as a treatment for prostate cancer. This nomogram uses numerous variables, such as age, weight index, blood pressure data, history of cardiovascular disease, tumor size, tumor grade and serum prostatic acid phosphatase to predict survival. (F. Harrell et al., Statistics in Medicine 15:361-387, 1996).
However, none of the postoperative models currently available predict probability of recurrence. Moreover, prior to the present invention, there has been no method or means to predict the probability of treatment failure following surgery, defined as a rising PSA level, following radical prostatectomy for clinically localized prostate cancer. Such risk profiles would be very useful in providing meaningful information to a patient making a decision among courses of therapy. Such a tool would provide the patient with his probability of recurrence instead of a relative risk which is more easily comprehended. While the relative risk informs the patient of his risk of recurring relative to another patient with certain characteristics, the actual probability should more greatly facilitate decision making for the patient.
Therefore, a need has arisen for a method and apparatus to accurately predict prior to surgery the likelihood of recurrence in an individual diagnosed with prostate cancer following radical prostatectomy, using routinely available clinical variables. In addition, a need has arisen for a method and apparatus for accurately predicting probability of recurrence post-prostatectomy, using data collected and available immediately postoperatively, to evaluate whether adjuvant therapy may be warranted before PSA begins to rise.
The present invention is directed to methods and apparatuses for predicting probability of disease recurrence following radical prostatectomy using routinely performed and available factors. The invention includes nomograms that can be used preoperatively and postoperatively to aid in selection of an appropriate course or courses of therapy.
One embodiment of the invention is directed to a method for predicting probability of recurrence of prostatic cancer following radical prostatectomy in a patient diagnosed as having prostatic cancer. This method comprises the steps of correlating a selected set of preoperative factors determined for each of a plurality of persons previously diagnosed with prostatic cancer and having been treated by radical prostatectomy with the incidence of recurrence of prostatic cancer for each person of said plurality of persons to generate a functional representation of the correlation, wherein said selected set of preoperative factors comprises pretreatment PSA level, combined Gleason grade in the biopsy specimen and clinical stage, and matching an identical set of preoperative factors determined from the patient diagnosed as having prostatic cancer to the functional representation to predict the probability of recurrence of prostatic cancer in the patient following radical prostatectomy. In another embodiment, biopsy Gleason sum may be used instead of combined Gleason grade. In another embodiment, the factors may further comprise one or more of the following: total length of cancer in the biopsy cores; maximum cancer length in a core; and apoptotic index.
Another embodiment of the invention is directed to a postoperative method for predicting probability of recurrence of prostatic cancer in a patient who has previously undergone a radical prostatectomy. This method comprises the steps of correlating a selected set of factors determined for each of a plurality of persons previously diagnosed with prostatic cancer with the incidence of recurrence of prostatic cancer for each person of said plurality to generate a functional representation of the correlation, wherein said selected set of factors comprises preoperative PSA level, specimen Gleason sum, prostatic capsular invasion level, surgical margin status, presence of seminal vesicle invasion, and lymph node status, wherein said plurality of persons comprises men having undergone radical prostatectomy, and matching an identical set of factors determined from the patient to the functional representation to predict the probability of recurrence of prostatic cancer for the patient.
Additional embodiments of the invention are directed to nomograms for determining a preoperative probability of prostatic cancer recurrence such as those depicted in FIGS. 2A and 2B and methods of using these nomograms to predict a patient""s prognosis. One such method predicts a patient""s preoperative prognosis by matching a patient-specific set of preoperative factors comprising pretreatment PSA level, clinical stage, and combined Gleason grade to the nomogram depicted in FIG. 2A or FIG. 2B and determining the preoperative prognosis of the patient.
Additional embodiments of the invention are directed to a nomogram for determining a postoperative probability of prostatic cancer recurrence such as depicted in FIG. 5 and methods of using this nomogram to predict a patient""s prognosis. One such method predicts a patient""s postoperative prognosis following radical prostatectomy by matching a patient-specific set of factors comprising the patient""s preoperative PSA level, specimen Gleason sum, prostatic capsular invasion level, surgical margin status, presence of seminal vesicle invasion, and lymph node status to the nomogram depicted in FIG. 5 and determining the prognosis of the patient.
Another embodiment of the invention is directed to a method for determining a need for an adjuvant therapy in a patient following radical prostatectomy comprising the steps of determining a set of factors on the patient, the set of factors comprising the patient""s preoperative PSA level, specimen Gleason sum, prostatic capsular invasion level, surgical margin status, presence of seminal vesicle invasion, and lymph node status; and matching the set of factors to the nomogram depicted in FIG. 5 to determine whether the adjuvant therapy is needed in view of the probability of recurrence.
Another embodiment of the invention is directed to an apparatus for predicting probability of disease recurrence in a patient with prostatic cancer following a radical prostatectomy, wherein the apparatus comprises a correlation of preoperative factors determined for each of a plurality of persons previously diagnosed with prostatic cancer and having been treated by radical prostatectomy with incidence of recurrence of prostatic cancer for each person of said plurality of persons, wherein said selected set of preoperative factors comprises pretreatment PSA level, combined Gleason grade in the biopsy specimen and clinical stage; and a means for matching an identical set of preoperative factors determined from the patient diagnosed as having prostatic cancer to the correlation to predict the probability of recurrence of prostatic cancer in the patient following radical prostatectomy.
Another embodiment of the invention is directed to an apparatus for predicting probability of disease recurrence in a patient with prostatic cancer following a radical prostatectomy, wherein the apparatus comprises: a correlation of clinical and pathological factors determined for each of a plurality of persons previously diagnosed with prostatic cancer and having been treated by radical prostatectomy with incidence of recurrence of prostatic cancer for each person of said plurality of persons wherein said selected set of factors comprises preoperative PSA level, specimen Gleason sum, prostatic capsular invasion level, surgical margin status, presence of seminal vesicle invasion, and lymph node status; and a means for matching an identical set of factors determined from the patient diagnosed as having prostatic cancer to the correlation to predict the probability of recurrence of prostatic cancer in the patient following radical prostatectomy.
Still another embodiment of the invention is directed to a nomogram for the graphic representation of the probability that a patient with prostate cancer will remain free of disease following radical prostatectomy comprising a substrate or a solid support and a set of indicia on the substrate or solid support, the indicia comprising a pretreatment PSA level line, a clinical stage line, a combined Gleason grade line, a points line, a total points line and a predictor line, wherein said pretreatment PSA level line, clinical stage line and combined Gleason grade line each have values on a scale which can be correlated with values on a scale on the points line, and wherein said total points line has values on a scale which may be correlated with values on a scale on the predictor line, such that the value of each of the points correlating with the patient""s pretreatment PSA level, combined Gleason grade, and clinical stage can be added together to yield a total points value, and the total points value may be correlated with the predictor line to predict the probability of recurrence.
Still another embodiment of the invention is directed to a nomogram for the graphic representation of the probability that a patient with prostate cancer will remain free of disease following radical prostatectomy comprising a substrate or solid support and a set of indicia on the substrate or solid support, the indicia comprising a preoperative PSA level line, a specimen Gleason sum line, a prostatic capsular invasion level line, a surgical margin status line, a presence of seminal vesicle invasion line, a lymph node status line, a points line, a total points line and a predictor line, wherein said preoperative PSA level line, specimen Gleason sum line, prostatic capsular invasion level line, surgical margin status line, presence of seminal vesicle invasion line, and lymph node status line each have values on a scale which can be correlated with values on a scale on the points line, and wherein said total points line has values on a scale which may be correlated with values on a scale on the predictor line, such that the value of each of the points correlating with the patient""s preoperative PSA level, specimen Gleason sum, prostatic capsular invasion level, surgical margin status, presence of seminal vesicle invasion, and lymph node status can be added together to yield a total points value, and the total points value can be correlated with the predictor line to predict the probability of recurrence.
As will be clear to those of skill in the art, the present invention may be modified to incorporate additional or fewer clinical, pathological and other variables. The invention may also be modified so as to allow prediction of the probability of recurrence after a variety of one or more therapies. As will further be clear to those of skill in the art, the invention may be embodied in any desired computerized format, including, but not limited to, those formats discussed below.
A further embodiment of the invention is directed to an apparatus for predicting a quantitative probability of disease recurrence in a patient with prostatic cancer following an identified therapy, in which the apparatus comprises a correlation of factors determined for each of a plurality of persons previously diagnosed with prostatic cancer and having been treated by the identified therapy with incidence of recurrence of prostatic cancer for each person of the plurality of persons. The apparatus further comprises a means for comparing an identical set of factors determined from the patient diagnosed as having prostatic cancer to the correlation to predict the quantitative probability of recurrence of prostatic cancer in the patient following the identified therapy. Preferably, the selected set of factors comprises two or more factors selected from the group consisting of pretreatment PSA level, combined Gleason grade, specimen Gleason sum, clinical stage, surgical margin status, prostatic capsular invasion level, extraprostatic extension, level of extraprostatic extension, apoptotic index, maximum cancer length in a core, total length of cancer in the biopsy cores, percent of cores positive, percent of cancer in one or more cores, percent of high grade cancer in one or more cores, total tumor volume, zone of location of the cancer, presence of seminal vesicle invasion, type of seminal vesicle invasion, p53, Ki-67, p27, DNA ploidy status, lymph node status, and lymphovascular invasion. Identified therapies include, but are not limited to, radical prostatectomy, radiation therapy, brachytherapy, hormonal therapy, cryotherapy, chemotherapy and combinations thereof.
The apparatus may assume a computerized or non-computerized form. For example, the apparatus may comprise a nomogram. In still another embodiment, the nomogram comprises a graphic representation which is disposed on a laminated card or other substrate. Alternately, the nomogram may be computerized and stored in a memory. Useful memory formats include, but are not limited to, random access memory, read-only memory, disk, virtual memory, processors, and the like. The nomogram may be stored in a database and may be accessible by multiple users.
The apparatus may further comprise a display that displays the quantitive probability of recurrence of prostatic cancer. The display may comprise a computer monitor, CRT, digital screen, LED, LCD, X-ray, compressed digitized image such as JPEG, MPEG, etc. video image or a hand held device (i.e. a Palm Pilot(trademark), calculator, etc). The display may be separated from the means for comparing, such that the display receives the quantitative probability of recurrence of prostatic cancer from the useful memory.
The apparatus may further comprise a database, which stores the correlation of factors and is accessible by the means for comparing. The apparatus also may comprise an input device that inputs the identical set of factors determined from the patient diagnosed as having prostatic cancer into the apparatus. Useful input devices include, but are not limited to a keypad, keyboard, stored data, touch screen, voice activated system, downloadable program or data, digital interface, hand-held device, or infra-red signal device. The input device may optionally store the identical set of factors in a means for storing that is accessible by the means for comparing. This means for storing may comprise include, but are not limited to, random access memory, read-only memory, disk, virtual memory, processors, and the like. Further, data may be stored in paper form on charts or graphs, or the like.
The apparatus may further comprise a transmission medium for transmitting the selected set of factors. This transmission medium may be coupled to the means for comparing and the correlation of factors. Additionally, or alternately, the apparatus may comprise a transmission medium for transmitting the identical set of factors determined from the patient diagnosed as having prostatic cancer. This transmission medium may be coupled to the means for comparing and the correlation of factors. For example, the transmission medium may include a global communication network, such as the Internet. Such a network may be accessed by means of personal computers or internet appliances, or the like.
The means for comparing may comprise a multi-purpose or dedicated processor. For example, the means for comparing may include an object oriented program having libraries, the libraries storing the correlation of factors. Alternately, apparatus according to the present invention may comprise storing means for storing the nomogram, means for inputting the identical set of factors determined from the patient into the apparatus, and display means for displaying the quantitive probability of recurrence of prostatic cancer. The storing means may comprise any suitable device, such as those described earlier, including, but not limited to, random access memory, read-only memory, one or more disks, virtual memory, processors, and the like. The means for inputting may comprise any suitable device, such as those described earlier, including, but not limited to, keypad, keyboard, stored data, touch screen, voice activated system, downloadable program or data, digital interface, hand-held device, or infra-red signal device. The display means may comprise any suitable device, such as those described earlier, including, but not limited to, a computer monitor, CRT, digital screen, LED, LCD, X-ray, compressed digitized image such as JPEG, MPEG, etc., video image, or hand held devices (i.e. Palm Pilot(trademark), calculator, etc).
Still another embodiment of the invention is directed to an apparatus for predicting a quantitative probability of disease recurrence in a patient with prostatic cancer following a radical prostatectomy. The apparatus comprises a correlation of clinical and pathological factors determined for each of a plurality of persons previously diagnosed with prostatic cancer and having been treated by radical prostatectomy with incidence of recurrence of prostatic cancer for each person of the plurality of persons, wherein the selected set of factors comprises specimen Gleason sum, surgical margin status, presence of seminal vesicle invasion, and lymph node status, and a means for comparing an identical set of factors determined from the patient diagnosed as having prostatic cancer to the correlation to predict the quantitative probability of recurrence of prostatic cancer in the patient following radical prostatectomy.
Yet a further embodiment of the invention is directed to a nomogram for the graphic representation of a quantitative probability that a patient with prostate cancer will remain free of disease following radical prostatectomy. The nomogram comprises a plurality of scales and a solid support, the plurality of scales being disposed on the support and comprising a specimen Gleason sum scale, a surgical margin status scale, a presence of seminal vesicle invasion scale, a lymph node status scale, a points scale, a total points scale and a predictor scale, wherein the specimen Gleason sum scale, surgical margin status scale, presence of seminal vesicle invasion scale, and lymph node status scale each have values on the scales, and wherein the specimen Gleason sum scale, the surgical margin status scale, the presence of seminal vesicle invasion scale, and the lymph node status scale are disposed
on the solid support with respect to the points scale so that each of the values on the specimen Gleason sum scale, the surgical margin status scale, the presence of seminal vesicle invasion scale, and the lymph node status scale can be correlated with values on the points scale, and wherein the total points scale has values on the total points scale and wherein the total points scale is disposed on the solid support with respect to the predictor scale so that the values on the total points scale may be correlated with values on the predictor scale, such that the values on the points scale correlating with the patient""s specimen Gleason sum, surgical margin status, presence of seminal vesicle invasion, and lymph node status may be added together to yield a total points value, and the total points value may be correlated with the predictor scale to predict the quantitative probability of recurrence. This nomogram may be embodied in a computerized form, and incorporate the various elements as disclosed above. For example, the nomogram may be stored in a memory. The nomogram may comprise a display that displays the nomogram.
Another embodiment is directed to a method to predict a postoperative prognosis in a patient following radical prostatectomy, comprising the steps of determining a set of factors comprising the patient""s specimen Gleason sum, surgical margin status, presence of seminal vesicle invasion, and lymph node status, matching the factors to the values on the specimen Gleason sum scale, the surgical margin status scale, the presence of seminal vesicle invasion scale and the lymph node status scale of the nomogram of the previous embodiment, determining a separate point value for each of the factors, adding the separate point values together to yield a total points value, and correlating the total points value with a value on the predictor scale of the nomogram to determine the prognosis of the patient.
Still a further embodiment is directed to a method for predicting a quantitative probability of recurrence of prostatic cancer in a patient following treatment with an identified therapy comprising the steps of correlating a selected set of factors determined for each of a plurality of persons previously diagnosed with prostatic cancer and having been treated by the identified therapy with incidence of recurrence of prostatic cancer for each person of the plurality of persons to generate a functional representation of the correlation, wherein the functional representation of the correlation comprises a separate factor evaluation system for each of the factors, and wherein each of the factor evaluation systems provides a value corresponding with a status of the corresponding factor, which value may be summed with values corresponding to the status of the other factors in the selected set to derive a quantitative probability of recurrence of prostatic cancer following the identified therapy, determining the status of an identical set of factors for the patient, applying the status of each of the patient""s set of factors to the corresponding factor evaluation system to determine a patient value for each of the factors, and summing the patient""s values to derive the quantitative probability of recurrence of prostatic cancer in the patient following the identified therapy. Preferably, the selected set of factors comprises at least two factors selected from the group consisting of pretreatment PSA level, combined Gleason grade, specimen Gleason sum, clinical stage, surgical margin status, prostatic capsular invasion level, extraprostatic extension, level of extraprostatic extension, apoptotic index, maximum cancer length in a core, total length of cancer in the biopsy cores, percent of cores positive, percent of cancer in one or more cores, percent of high grade cancer in one or more cores, total tumor volume, zone of location of the cancer, presence of seminal vesicle invasion, type of seminal vesicle invasion, p53, Ki-67, p27, DNA ploidy status, lymph node status, and lymphovascular invasion.
The factor evaluation systems preferably comprise a scale having values corresponding to status of each factor and the step of applying comprises matching the patient""s status for each of the factors to its status on the corresponding scale to determine the patient""s values for each of the factors. The functional representation may be a nomogram. The method may further comprise the step of displaying the functional representation on a display, such as the display devices previously described. The method may further comprise the step of inputting the identical set of factors for the patient with an input device.
The steps of correlating, determining, applying, and summing maybe executed by one or more processors or by one or more virtual computer programs. The correlating step may include accessing a memory storing the selected set of factors. The correlating step may include generating the functional representation and displaying the functional representation on a display. The displaying step may include transmitting the functional representation to the display. Alternately, the displaying step may include downloading the functional representation from a source. The source may comprise a an internet service provider, website, memory, database, internet, intranet, ethernet, or mainframe, or the like. The determining step may include accessing a memory storing the identical set of factors. The method may further comprise the step of storing any of the set of factors to a memory or to a database. The method may further comprise the step of transmitting the quantitative probability of recurrence of prostatic cancer. In this method, the identified therapy may be any desired therapy, including, but not limited to radical prostatectomy, radiation therapy, brachytherapy, hormonal therapy, cryotherapy, chemotherapy and combinations thereof.
Another embodiment is directed to a method for predicting a quantitative probability of recurrence of prostatic cancer following radical prostatectomy in a patient diagnosed as having prostatic cancer comprising the steps of: correlating a selected set of preoperative factors determined for each of a plurality of persons previously diagnosed with prostatic cancer and having been treated by radical prostatectomy with incidence of recurrence of prostatic cancer for each person of the plurality of persons to generate a functional representation of the correlation, wherein the selected set of preoperative factors comprises pretreatment PSA level, combined Gleason grade, clinical stage, and one or more supplemental factors selected from the group consisting of apoptotic index, maximum cancer length in a core, percent of cores positive, percent of cancer in one or more cores, percent of high grade cancer in one or more cores and total length of cancer in the biopsy cores, wherein the functional representation of the correlation comprises a pretreatment PSA level scale, a clinical stage scale, a combined Gleason grade scale, one or more supplemental factor scales for each of the one or more supplemental factors, a points scale, a total points scale, and a predictor scale, and wherein the pretreatment PSA level scale, the clinical stage scale, the combined Gleason grade scale and the one or more supplemental factors scales each have values on the scales which can be correlated with values on the points scale, and wherein the total points scale has values which may be correlated with values on the predictor scale; determining an identical set of preoperative factors for the patient; matching the patient""s pretreatment PSA level to a corresponding value on the pretreatment PSA level scale, and determining a first point value from the corresponding value on the points scale; matching the patient""s combined Gleason grade to a corresponding value on the combined Gleason grade scale, and determining a second point value from the corresponding value on the points scale; matching the patient""s clinical stage to a corresponding value on the clinical stage scale, and determining a third point value from the corresponding value on the points scale; matching the patient""s one or more supplemental factors to one or more corresponding values on the one or more supplemental factor scales to determine one or more supplemental point values on the points scale; adding the first, second and third and one or more supplemental point values together to get a patient total points value; matching the patient total points value to a corresponding value on the total points scale; and correlating the corresponding value on the total points scale with a value on the predictor scale to predict the quantitative probability of recurrence of prostatic cancer in the patient following radical prostatectomy.
The steps of this method may be executed on a computer processing device. The computer processing device preferably displays the values to a user. Alternately, the steps are executed on an embedded processor.
In still another embodiment, the invention is directed to a postoperative method for predicting a quantitative probability of recurrence of prostatic cancer in a patient who has previously undergone a radical prostatectomy comprising the steps of: correlating a selected set of factors determined for each of a plurality of persons previously diagnosed with prostatic cancer with incidence of recurrence of prostatic cancer for each person of the plurality to generate a functional representation of the correlation, wherein the selected set of factors comprises specimen Gleason sum, surgical margin status, presence of seminal vesicle invasion, and lymph node status, wherein the plurality of persons comprises men having undergone radical prostatectomy, wherein the functional representation of the correlation comprises a specimen Gleason sum scale, a surgical margin status scale, a presence of seminal vesicle invasion scale, a lymph node status scale, a points scale, a total points scale, and a predictor scale, and wherein the specimen Gleason sum scale, the surgical margin status scale, the presence of seminal vesicle invasion scale, and the lymph node status scale each have values on the scales which can be correlated with values on the points scale, and wherein the total points scale has values on the scale which may be correlated with values on the predictor scale; determining an identical set of factors for the patient; matching the patient""s specimen Gleason sum to a corresponding value on the specimen Gleason sum scale, and determining a first point value from the corresponding value on the points scale; matching the patient""s surgical margin status to a corresponding value on the surgical margin status scale, and determining a second point value from the corresponding value on the points scale; matching the patient""s presence of seminal vesicle invasion to a corresponding value on the presence of seminal vesicle invasion scale, and determining a third point value from the corresponding value on the points scale; matching the patient""s lymph node status to a corresponding value on the lymph node status scale, and determining a fourth point value from the corresponding value on the points scale; adding the first, second, third, and fourth point values together to get a patient total points value; matching the patient total points value to a corresponding value on the total points scale; and correlating the corresponding value on the total points scale with a value on the predictor scale to predict the quantitative probability of recurrence of prostatic cancer for the patient.
Optionally, the selected set of factors further comprises one or more supplemental factors selected from the group consisting of total tumor volume, pretreatment PSA, prostatic capsular invasion, zone of location of the cancer, p53, Ki-67, p27, level of extraprostatic extension, DNA ploidy status, type of seminal vesicle invasion, clinical stage and lymphovascular invasion and the functional representation further comprises one or more supplemental factor scales for each of the one or more supplemental factors, the one or more supplemental factor scales each having values on the scales which can be correlated with the values on the points scale, and wherein the method further comprises the steps of: determining the patient""s one or more supplemental factors; matching the patient""s one or more supplemental factors to one or more corresponding values on the one or more supplemental factor scales to determine one or more supplemental point values on the points scale; and adding the one or more supplemental point values to the first, second, third, and fourth point values to determine the patient total points value.
As with the previous embodiment, the steps may be executed on a computer processing device, such as one in which the computer processing device displays the values to a user. The steps may be executed on an embedded processor.
In a further embodiment, the intention is directed to a method of using a computer processor for predicting a quantitative probability of recurrence of prostatic cancer in a patient following treatment with an identified therapy comprising the steps of: correlating a selected set of factors determined for each of a plurality of persons previously diagnosed with prostatic cancer and having been treated by the identified therapy with incidence of recurrence of prostatic cancer for each person of the plurality of persons to generate a functional representation of the correlation using the computer processor, wherein the functional representation of the correlation comprises a separate factor evaluation system for each of the factors, and wherein each of the factor evaluation systems provides a value corresponding with a status of the corresponding factor, which value may be summed with values corresponding to the status of the other factors in the selected set to derive a quantitative probability of recurrence of prostatic cancer following the identified therapy; determining the status of an identical set of factors for the patient using the computer processor; applying the status of each of the patient""s set of factors to the corresponding factor evaluation system to determine a patient value for each of the factors using the computer processor; and summing the patient""s values to derive the quantitative probability of recurrence of prostatic cancer in the patient following the identified therapy. Preferably, the selected set of factors comprises at least two factors selected from the group consisting of pretreatment PSA level, combined Gleason grade, specimen Gleason sum, clinical stage, surgical margin status, prostatic capsular invasion level, extraprostatic extension, level of extraprostatic extension, apoptotic index, maximum cancer length in a core, total length of cancer in the biopsy cores, percent of cores positive, percent of cancer in one or more cores, percent of high grade cancer in one or more cores, total tumor volume, zone of location of the cancer, presence of seminal vesicle invasion, type of seminal vesicle invasion, p53, Ki-67, p27, DNA ploidy status, lymph node status, and lymphovascular invasion
A memory may be coupled to or integral with the computer processor. An input device may be coupled to the computer processor. A display may be coupled to the computer processor which display receives data from the computer processor.
In still another embodiment is directed to a computerized method for predicting a quantitative probability of recurrence of prostatic cancer in a patient following treatment with an identified therapy comprising the steps of: correlating a selected set of factors determined for each of a plurality of persons previously diagnosed with prostatic cancer and having been treated by the identified therapy with incidence of recurrence of prostatic cancer for each person of the plurality of persons to generate a functional representation of the correlation, wherein the functional representation of the correlation comprises a separate factor evaluation system for each of the factors, and wherein each of the factor evaluation systems provides a value corresponding with a status of the corresponding factor, which value may be summed with values corresponding to the status of the other factors in the selected set to derive a quantitative probability of recurrence of prostatic cancer following the identified therapy; determining the status of an identical set of factors for the patient; applying the status of each of the patient""s set of factors to the corresponding factor evaluation system to determine a patient value for each of the factors; and summing the patient""s values to derive the quantitative probability of recurrence of prostatic cancer in the patient following the identified therapy. Preferably, the selected set of factors comprises at least two factors selected from the group consisting of pretreatment PSA level, combined Gleason grade, specimen Gleason sum, clinical stage, surgical margin status, prostatic capsular invasion level, extraprostatic extension, level of extraprostatic extension, apoptotic index, maximum cancer length in a core, total length of cancer in the biopsy cores, percent of cores positive, percent of cancer in one or more cores, percent of high grade cancer in one or more cores, total tumor volume, zone of location of the cancer, presence of seminal vesicle invasion, type of seminal vesicle invasion, p53, Ki-67, p27, DNA ploidy status, lymph node status, and lymphovascular invasion.
Other embodiments and advantages of the invention are set forth, in part, in the description which follows and, in part, will be obvious from this description and may be learned from the practice of the invention.